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fix ticket 1131

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master
Per.Andreas.Brodtkorb 12 years ago
parent 14d5dc29b2
commit 4087577ec0
2 changed files with 165 additions and 63 deletions
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159
pywafo/src/wafo/stats/distributions.py
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@ -581,7 +581,6 @@ class rv_frozen(object):
args, loc0 = dist.fix_loc(args, loc0)
self.par = args + (loc0,)
def pdf(self, x):
''' Probability density function at x of the given RV.'''
return self.dist.pdf(x, *self.par)
@ -1931,25 +1930,16 @@ class rv_continuous(rv_generic):
q, loc, scale = map(asarray,(q, loc, scale))
args = tuple(map(asarray,args))
cond0 = self._argcheck(*args) & (scale > 0) & (loc==loc)
cond1 = (q > 0) & (q < 1)
cond2 = (q==1) & cond0
cond1 = (0 < q) & (q < 1)
cond2 = cond0 & (q==0)
cond3 = cond0 & (q==1)
cond = cond0 & cond1
output = valarray(shape(cond),value=self.a*scale + loc)
place(output,(1-cond0)+(1-cond1)*(q!=0.0), self.badvalue)
# begin CAT patch 2: 17 MARCH 2010
# If expr in last arg has shape(N,1), place() tries to jam an
# array into each cell in output with cond2 == True.
# Causes exception even when (not any(cond2)) holds.
#WASplace(output,cond2,self.b*scale + loc)
proxy_value = self.b * scale + loc
if product(shape(proxy_value)) != 1:
proxy_value = extract(cond2, proxy_value * cond2)
place(output, cond2, proxy_value)
# end CAT patch 2: 17 MARCH 2010
# rest of method unchanged...
output = valarray(shape(cond), value=self.badvalue)
place(output, cond2, argsreduce(cond2, self.a * scale + loc)[0])
place(output, cond3, argsreduce(cond3, self.b * scale + loc)[0])
if any(cond): #call only if at least 1 entry
goodargs = argsreduce(cond, *((q,)+args+(scale,loc)))
scale, loc, goodargs = goodargs[-2], goodargs[-1], goodargs[:-2]
@ -1985,27 +1975,15 @@ class rv_continuous(rv_generic):
q, loc, scale = map(asarray,(q, loc, scale))
args = tuple(map(asarray,args))
cond0 = self._argcheck(*args) & (scale > 0) & (loc==loc)
cond1 = (q > 0) & (q < 1)
cond2 = (q==1) & cond0
cond = cond0 & cond1
initial_value = self.b * scale + loc
output = valarray(shape(cond), value=initial_value)
#place(output,(1-cond0)*(cond1==cond1), self.badvalue)
place(output,(1-cond0)*(cond1==cond1)+(1-cond1)*(q!=0.0), self.badvalue)
cond1 = (0 < q) & (q < 1)
cond2 = cond0 & (q==1)
cond3 = cond0 & (q==0)
# begin CAT patch 4: 18 MARCH 2010
# If expr in last arg has shape=(N,1), place() tries to jam a large
# array into each cell in output where cond2==True.
# Causes exception even if cond2 is never True.
# This statement also has the unfortunate side effect of being wrong
# when the support of the distribution is bounded below.
#WASplace(output,cond2,self.a)
cond = cond0 & cond1
output = valarray(shape(cond), value=self.badvalue)
proxy_value = self.a * scale + loc
if product(shape(proxy_value)) != 1:
proxy_value = extract(cond2, proxy_value * cond2)
place(output, cond2, proxy_value)
place(output, cond2, argsreduce(cond2, self.a * scale + loc)[0])
place(output, cond3, argsreduce(cond3, self.b * scale + loc)[0])
if any(cond): #call only if at least 1 entry
goodargs = argsreduce(cond, *((q,)+args+(scale,loc))) #PB replace 1-q by q
@ -2209,12 +2187,11 @@ class rv_continuous(rv_generic):
if not self._argcheck(*args) or scale <= 0:
return inf
x = arr((x - loc) / scale)
cond0 = (x <= self.a) | (x >= self.b)
cond0 = (x <= self.a) | (self.b <= x)
Nbad = sum(cond0)
if Nbad>0:
x = argsreduce(1 - cond0, *((x,)))[0]
#return inf
#else:
x = argsreduce( ~cond0, x)[0]
lowertail = True
if lowertail:
@ -2260,10 +2237,9 @@ class rv_continuous(rv_generic):
def _nnlf(self, x, *args):
loginf = -log(floatinfo.machar.xmin)
logpdf = self._logpdf(x, *args).clip(min= -loginf)
logpdf = self._logpdf(x, *args).clip(min= -100*loginf)
return -sum(logpdf, axis=0)
def nnlf(self, theta, x):
''' Return negative loglikelihood function, i.e., - sum (log pdf(x, theta),axis=0)
where theta are the parameters (including loc and scale)
@ -2277,18 +2253,15 @@ class rv_continuous(rv_generic):
if not self._argcheck(*args) or scale <= 0:
return inf
x = asarray((x-loc) / scale)
cond0 = (x <= self.a) | (x >= self.b)
if (any(cond0)):
# old call: return inf
goodargs = argsreduce(1 - cond0, *((x,)))
goodargs = tuple(goodargs + list(args))
N = len(x)
cond0 = (x <= self.a) | (self.b <= x)
Nbad = sum(cond0)
xmax = floatinfo.machar.xmax
return self._nnlf(*goodargs) + N * (log(scale) + Nbad * 100.0 * log(xmax))
else:
loginf = log(floatinfo.machar.xmax)
if Nbad>0:
x = argsreduce(~cond0, x)[0]
N = len(x)
return self._nnlf(x, *args) + N*log(scale)
return self._nnlf(x, *args) + N*log(scale) + Nbad * 100.0 * loginf
def hessian_nlogps(self, theta, data, eps=None):
''' approximate hessian of nlogps where theta are the parameters (including loc and scale)
'''
@ -2529,6 +2502,7 @@ class rv_continuous(rv_generic):
except AttributeError:
raise ValueError("%s is not a valid optimizer" % optimizer)
vals = optimizer(func,x0,args=(ravel(data),),disp=0)
if restore is not None:
vals = restore(args, vals)
vals = tuple(vals)
@ -2613,6 +2587,10 @@ class rv_continuous(rv_generic):
mu2hat = tmp.var()
Shat = sqrt(mu2hat / mu2)
Lhat = muhat - Shat*mu
if not np.isfinite(Lhat):
Lhat = 0
if not (np.isfinite(Shat) and (0 < Shat)) :
Shat = 1
return Lhat, Shat
@np.deprecate
@ -3093,6 +3071,15 @@ class bradford_gen(rv_continuous):
def _entropy(self, c):
k = log1p(c)
return k / 2.0 - log(c / k)
def _fitstart(self, data):
loc = data.min()-1e-4
scale = (data-loc).max()
m = np.mean((data-loc)/scale)
fun = lambda c : (c-log1p(c))/(c*log1p(c)) - m
res = optimize.root(fun, 0.3)
c = res.x
return c, loc, scale
bradford = bradford_gen(a=0.0, b=1.0, name='bradford', shapes='c')
@ -3249,6 +3236,12 @@ class chi_gen(rv_continuous):
g2 = 2*df*(1.0-df)-6*mu**4 + 4*mu**2 * (2*df-1)
g2 /= asarray(mu2**2.0)
return mu, mu2, g1, g2
def _fitstart(self, data):
m = data.mean()
v = data.var()
# Supply a starting guess with method of moments:
df = max(np.round(v+m**2),1)
return super(chi_gen, self)._fitstart(data, args=(df,))
chi = chi_gen(a=0.0, name='chi', shapes='df')
@ -3293,6 +3286,12 @@ class chi2_gen(rv_continuous):
g1 = 2*sqrt(2.0/df)
g2 = 12.0/df
return mu, mu2, g1, g2
def _fitstart(self, data):
m = data.mean()
v = data.var()
# Supply a starting guess with method of moments:
df = max(np.round((m+v/2)/2),1)
return super(chi2_gen, self)._fitstart(data, args=(df,))
chi2 = chi2_gen(a=0.0, name='chi2', shapes='df')
@ -3711,6 +3710,14 @@ class f_gen(rv_continuous):
g2 = 3/(2*v2-16)*(8+g1*g1*(v2-6))
g2 = where(v2 > 8, g2, nan)
return mu, mu2, g1, g2
def _fitstart(self, data):
m = data.mean()
v = data.var()
# Supply a starting guess with method of moments:
dfd = max(np.round(2*m/(m-1)), 5)
dfn = max(np.round(2*dfd*dfd*(dfd-2)/(v*(dfd-4)*(dfd-2)**2 - 2*dfd*dfd)), 1)
return super(f_gen, self)._fitstart(data, args=(dfn,dfd,))
f = f_gen(a=0.0, name='f', shapes="dfn, dfd")
@ -3810,11 +3817,16 @@ class frechet_r_gen(rv_continuous):
return special.gamma(1.0+n*1.0/c)
def _entropy(self, c):
return -_EULER / c - log(c) + _EULER + 1
def _fitstart(self, data):
loc = data.min() - 0.01 #*np.std(data)
chat = 1./(6**(1/2)/pi*np.std(log(data-loc)))
scale = np.mean((data-loc)**chat)**(1./chat)
return chat, loc, scale
frechet_r = frechet_r_gen(a=0.0, name='frechet_r', shapes='c')
weibull_min = frechet_r_gen(a=0.0, name='weibull_min', shapes='c')
class frechet_l_gen(rv_continuous):
"""A Frechet left (or Weibull maximum) continuous random variable.
@ -3851,6 +3863,11 @@ class frechet_l_gen(rv_continuous):
return sgn * val
def _entropy(self, c):
return -_EULER / c - log(c) + _EULER + 1
def _fitstart(self, data):
loc = data.max() + 0.1*np.std(data)
chat = 1./(6**(1/2)/pi*np.std(log(loc-data)))
scale = np.mean((loc-data)**chat)**(1./chat)
return chat, loc, scale
frechet_l = frechet_l_gen(b=0.0, name='frechet_l', shapes='c')
weibull_max = frechet_l_gen(b=0.0, name='weibull_max', shapes='c')
@ -4007,7 +4024,6 @@ class genpareto_gen(rv_continuous):
scale = m * ((m / s) ** 2 + 1) / 2
return shape, loc, scale
def hessian_nnlf(self, theta, x, eps=None):
try:
loc = theta[-2]
@ -5175,6 +5191,13 @@ class lognorm_gen(rv_continuous):
return mu, mu2, g1, g2
def _entropy(self, s):
return 0.5*(1+log(2*pi)+2*log(s))
def _fitstart(self, data):
scale = data.std()
loc = data.min()-0.001
logd = log(data-loc)
m = logd.mean()
s = sqrt((logd**2).mean() - m**2)
return s, loc, scale
lognorm = lognorm_gen(a=0.0, name='lognorm', shapes='s')
@ -5208,6 +5231,10 @@ class gilbrat_gen(lognorm_gen):
return lognorm_gen._stats(self, 1.0)
def _entropy(self):
return 0.5*log(2*pi) + 0.5
def _fitstart(self, data):
scale = data.std()
loc = data.min()-0.001
return loc, scale
gilbrat = gilbrat_gen(a=0.0, name='gilbrat')
@ -5313,6 +5340,7 @@ class nakagami_gen(rv_continuous):
g2 = -6*mu**4*nu + (8*nu-2)*mu**2-2*nu + 1
g2 /= nu*mu2**2.0
return mu, mu2, g1, g2
nakagami = nakagami_gen(a=0.0, name="nakagami", shapes='nu')
@ -5352,6 +5380,13 @@ class ncx2_gen(rv_continuous):
val = df + 2.0*nc
return df + nc, 2*val, sqrt(8)*(val+nc)/val**1.5, \
12.0*(val+2*nc)/val**2.0
def _fitstart(self, data):
m = data.mean()
v = data.var()
# Supply a starting guess with method of moments:
nc = (v/2-m)/2
df = m-nc
return super(ncx2_gen, self)._fitstart(data, args=(df, nc))
ncx2 = ncx2_gen(a=0.0, name='ncx2', shapes="df, nc")
@ -5407,6 +5442,13 @@ class ncf_gen(rv_continuous):
((dfn+nc/2.0)**2.0 + (dfn+nc)*(dfd-2.0)) / \
((dfd-2.0)**2.0 * (dfd-4.0)))
return mu, mu2, None, None
# def _fitstart(self, data):
# m = data.mean()
# v = data.var()
# # Supply a starting guess with method of moments:
# dfn = max(np.round(v+m**2),1)
# return super(ncf_gen, self)._fitstart(data, args=(dfn, dfd, nc))
ncf = ncf_gen(a=0.0, name='ncf', shapes="dfn, dfd, nc")
@ -8476,6 +8518,11 @@ def test_genpareto():
print(phat.par)
if __name__ == '__main__':
rv = reciprocal(0.2836212578535795, 1.2836212578535795)
vals = rv.ppf([0,1])
rv = genextreme(1.9556140678015677)
vals = rv.ppf([0,1])
import matplotlib
#matplotlib.interactive=False
import matplotlib.pyplot as plt

55
pywafo/src/wafo/stats/tests/test_distributions.py
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@ -68,6 +68,61 @@ def test_all_distributions():
args = tuple(1.0+rand(nargs))
yield check_distribution, dist, args, alpha
def test_ppf_and_isf_all_distributions():
for dist in dists:
distfunc = getattr(stats, dist)
nargs = distfunc.numargs
for check_fun in [check_distribution_ppf, check_distribution_isf]:
if dist == 'erlang':
args = (4,)+tuple(rand(2))
elif dist == 'frechet':
args = tuple(2*rand(1))+(0,)+tuple(2*rand(2))
elif dist == 'triang':
args = tuple(rand(nargs))
elif dist == 'reciprocal':
vals = rand(nargs)
vals[1] = vals[0] + 1.0
args = tuple(vals)
elif dist == 'vonmises':
yield check_fun, dist, (10,)
yield check_fun, dist, (101,)
args = tuple(1.0+rand(nargs))
else:
args = tuple(1.0+rand(nargs))
yield check_fun, dist, args
def check_distribution_ppf(diststr, args):
dist = getattr(stats, diststr)
n = dist.numargs
loc, scale = 0, 1
rv = dist(*args)
loc_scale = rv.par[n:]
if len(loc_scale)>0:
loc = loc_scale[0]
if len(loc_scale)>1:
scale = loc_scale[1]
limits = rv.ppf([0, 1]) #[1-1e-15, 1e-15])
true_limits = np.array([rv.dist.a*scale + loc, rv.dist.b*scale + loc])
assert_allclose(limits, true_limits, atol=1e-7, err_msg='Expected support for distribution')
def check_distribution_isf(diststr, args):
dist = getattr(stats, diststr)
n = dist.numargs
loc, scale = 0, 1
rv = dist(*args)
loc_scale = rv.par[n:]
if len(loc_scale)>0:
loc = loc_scale[0]
if len(loc_scale)>1:
scale = loc_scale[1]
limits = rv.isf([1, 0]) #[1-1e-15, 1e-15])
true_limits = np.array([rv.dist.a*scale + loc, rv.dist.b*scale + loc])
assert_allclose(limits, true_limits, atol=1e-7, err_msg='Expected support for distribution')
class TestFitMethod(TestCase):
skip = ['ncf']

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